import pandas as pd
from collections import deque
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import re
import json
import openai
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import StreamTableEnvironment
from pyflink.table.window import Tumble
import numpy as np
import torch
from arch import arch_model
import asyncio
from transformers import BitsAndBytesConfig
import jetson_inference
from stable_baselines3 import PPO


# 模拟的WebSocket客户端类，实际使用时需要替换为真实的实现
class WebSocketClient:
    def __init__(self):
        pass

    def connect(self):
        pass

    def receive_data(self):
        # 模拟接收数据，实际中应从真实的WebSocket连接接收数据
        return {
            "symbol": "AAPL",
            "price": 170.5,
            "volume": 100000,
            "open_interest": 5000,
            "news_text": "Apple reports strong earnings",
            "news_image": "apple_earnings.jpg"
        }


# 大模型策略生成模块（增强版）
class AdvancedLLMTradingStrategy:
    def __init__(self, model_name="gpt-4"):
        self.model_name = model_name
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.system_prompt = """
        你是一个专业的量化交易策略师，具备以下能力：
        1. 实时分析市场数据（价格、成交量、持仓量变化）
        2. 理解新闻情绪和技术指标
        3. 生成符合风险管理规则的交易策略
        4. 输出结构化的JSON格式策略
        """
        self.risk_constraints = {
            "max_position": 1000000,
            "stop_loss_pct": 0.02,
            "max_drawdown": 0.1
        }

    def generate_strategy(self, market_data):
        # 多模态数据处理（新增）
        if market_data.get("news_image"):
            image_analysis = self.analyze_news_image(market_data["news_image"])
            market_data["image_sentiment"] = image_analysis["sentiment"]

        # 动态调整温度值（参考）
        temperature = 0.5 if market_data.get("volatility", 0) > 5 else 0.7

        # 构建结构化提示（参考）
        prompt = f"""
        以下是市场数据：
        {json.dumps(market_data, indent=2)}

        请生成符合以下约束的日内交易策略：
        {json.dumps(self.risk_constraints, indent=2)}

        输出格式：JSON
        """

        # 调用GPT - 4 API（参考）
        response = openai.ChatCompletion.create(
            model=self.model_name,
            messages=[
                {"role": "system", "content": self.system_prompt},
                {"role": "user", "content": prompt}
            ],
            temperature=temperature,
            max_tokens=300
        )

        return self.parse_strategy(response["choices"][0]["message"]["content"])

    def analyze_news_image(self, image_path):
        # 多模态分析（参考）
        image_analyzer = pipeline("image-classification", model="google/vit-base-patch16-224")
        result = image_analyzer(image_path)
        return {"sentiment": result[0]["label"], "confidence": result[0]["score"]}

    def parse_strategy(self, response):
        try:
            return json.loads(response)
        except json.JSONDecodeError:
            return {"error": "Invalid JSON response from LLM"}


# 实时数据处理管道（优化版）
class RealTimeDataProcessor:
    def __init__(self):
        self.env = StreamExecutionEnvironment.get_execution_environment()
        self.t_env = StreamTableEnvironment.create(self.env)
        self.ohlcv_stream = self.t_env.from_elements([], ['symbol', 'price', 'volume', 'open_interest'])

    def process(self, raw_data):
        # 数据清洗与特征工程
        cleaned_data = self.clean_data(raw_data)
        features = self.extract_features(cleaned_data)

        # 流式计算技术指标（参考）
        self.ohlcv_stream = self.ohlcv_stream.union(
            self.t_env.from_elements([features], ['symbol', 'price', 'volume', 'open_interest'])
        )

        # 计算移动平均线
        windowed_table = self.ohlcv_stream.window(Tumble.over("30.seconds").on("rowtime"))
        ma30 = windowed_table.group_by("symbol").select("symbol, avg(price) as ma30")

        return self.t_env.to_pandas(ma30).iloc[-1].to_dict()

    def clean_data(self, raw_data):
        # 数据清洗逻辑
        return {
            "symbol": raw_data["symbol"],
            "price": float(raw_data["price"]),
            "volume": int(raw_data["volume"]),
            "open_interest": int(raw_data["open_interest"]),
            "news_text": raw_data.get("news_text", ""),
            "news_image": raw_data.get("news_image", None)
        }

    def extract_features(self, cleaned_data):
        # 特征工程示例
        return {
            "symbol": cleaned_data["symbol"],
            "price": cleaned_data["price"],
            "volume": cleaned_data["volume"],
            "open_interest": cleaned_data["open_interest"],
            "volatility": self.calculate_volatility(cleaned_data["price"]),
            "rowtime": pd.Timestamp.now()
        }

    def calculate_volatility(self, price):
        # 简单波动率计算
        return pd.Series([price]).pct_change().std()


# 动态风险管理系统（增强版）
class DynamicRiskManager:
    def __init__(self):
        self.var_model = None  # 初始化GARCH模型
        self.cvar_threshold = 0.05
        self.risk_constraints = {
            "max_position": 1000000,
            "stop_loss_pct": 0.02,
            "max_drawdown": 0.1
        }

    def initialize_risk_model(self, portfolio_returns):
        # 初始化风险模型（参考）
        self.var_model = arch_model(portfolio_returns, vol='GARCH', p=1, o=0, q=1)

    def update_risk_model(self, new_returns):
        # 动态更新风险模型
        if self.var_model:
            self.var_model.fit(update_freq=1)

    def calculate_risk_metrics(self, portfolio_value):
        # 计算风险指标（参考）
        var = self.var_model.forecast(horizon=1).variance.iloc[-1, 0]
        cvar = self.cvar_threshold * portfolio_value
        return {"var": var, "cvar": cvar}

    def check_strategy_risk(self, strategy, portfolio_value):
        # 策略风险校验
        if strategy.get("position_size", 0) * portfolio_value > self.risk_constraints["max_position"]:
            raise ValueError("Exceed position limit")
        if strategy.get("stop_loss", 0) < portfolio_value * (1 - self.risk_constraints["stop_loss_pct"]):
            raise ValueError("Invalid stop loss")


# 模型压缩与优化
class QuantizedModelLoader:
    def __init__(self):
        self.bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_quant_type='nf4',
            bnb_4bit_compute_dtype=torch.bfloat16
        )

    def load_model(self, model_name):
        # 加载低比特量化模型（参考）
        model = AutoModelForCausalLM.from_pretrained(
            model_name,
            quantization_config=self.bnb_config,
            device_map='auto'
        )
        return model


# 异步请求处理
class AsyncAPICaller:
    async def call_gpt4(self, prompt):
        # 异步调用GPT - 4 API（参考）
        loop = asyncio.get_event_loop()
        return await loop.run_in_executor(
            None,
            openai.ChatCompletion.create,
            {
                "model": "gpt-4",
                "messages": [{"role": "user", "content": prompt}],
                "temperature": 0.5
            }
        )


# 强化学习策略优化器
class RLStrategyOptimizer:
    def __init__(self):
        self.model = PPO("MlpPolicy", env=None)

    def optimize_strategy(self, historical_data):
        self.model.learn(total_timesteps=10000)
        return self.model.predict(historical_data)


# 合规性监控器
class ComplianceMonitor:
    def __init__(self):
        self.transaction_log = []

    def log_transaction(self, order):
        self.transaction_log.append({
            "timestamp": pd.Timestamp.now(),
            "order": order
        })


# 边缘推理
class EdgeInference:
    def __init__(self):
        self.model = jetson_inference.detectNet("ssd-mobilenet-v2")

    def process_image(self, image_path):
        img = jetson_inference.loadImage(image_path)
        detections = self.model.Detect(img)
        return detections


# 交易引擎类
class TradingEngine:
    def __init__(self):
        self.strategy_engine = AdvancedLLMTradingStrategy()
        self.data_processor = RealTimeDataProcessor()
        self.risk_manager = DynamicRiskManager()
        self.quant_loader = QuantizedModelLoader()
        self.rl_optimizer = RLStrategyOptimizer()
        self.compliance_monitor = ComplianceMonitor()
        self.edge_inference = EdgeInference()
        self.portfolio_value = 1000000
        self.quant_model = self.quant_loader.load_model("gpt-4")

    def run(self):
        while True:
            # 获取原始数据
            raw_data = WebSocketClient().receive_data()
            # 处理数据
            market_data = self.data_processor.process(raw_data)
            # 生成策略
            strategy = self.strategy_engine.generate_strategy(market_data)
            try:
                # 风险校验
                self.risk_manager.check_strategy_risk(strategy, self.portfolio_value)
                # 执行交易（模拟）
                print(f"Executing strategy: {strategy}")
                asyncio.run(AsyncAPICaller().call_gpt4("Update portfolio status"))
                self.compliance_monitor.log_transaction(strategy)
            except ValueError as e:
                print(f"Risk violation: {e}")


if __name__ == "__main__":
    engine = TradingEngine()
    engine.run()
